Modelling A.I. in Economics

Should You Buy NYCB^A Right Now? (Forecast)

Outlook: New York Community Bancorp Inc. Depositary shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Series A Noncumulative Perpetual Preferred Stock is assigned short-term Ba3 & long-term Ba3 estimated rating.
AUC Score : What is AUC Score?
Short-Term Revised1 :
Dominant Strategy : SellHold
Time series to forecast n: for Weeks2
Methodology : Modular Neural Network (Market News Sentiment Analysis)
Hypothesis Testing : Polynomial Regression
Surveillance : Major exchange and OTC

1The accuracy of the model is being monitored on a regular basis.(15-minute period)

2Time series is updated based on short-term trends.


Summary

New York Community Bancorp Inc. Depositary shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Series A Noncumulative Perpetual Preferred Stock prediction model is evaluated with Modular Neural Network (Market News Sentiment Analysis) and Polynomial Regression1,2,3,4 and it is concluded that the NYCB^A stock is predictable in the short/long term. A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.5 According to price forecasts for 8 Weeks period, the dominant strategy among neural network is: SellHold

Graph 25

Key Points

  1. Modular Neural Network (Market News Sentiment Analysis) for NYCB^A stock price prediction process.
  2. Polynomial Regression
  3. Prediction Modeling
  4. Market Risk
  5. Stock Forecast Based On a Predictive Algorithm

NYCB^A Stock Price Forecast

We consider New York Community Bancorp Inc. Depositary shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Series A Noncumulative Perpetual Preferred Stock Decision Process with Modular Neural Network (Market News Sentiment Analysis) where A is the set of discrete actions of NYCB^A stock holders, F is the set of discrete states, P : S × F × S → R is the transition probability distribution, R : S × F → R is the reaction function, and γ ∈ [0, 1] is a move factor for expectation.1,2,3,4


Sample Set: Neural Network
Stock/Index: NYCB^A New York Community Bancorp Inc. Depositary shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Series A Noncumulative Perpetual Preferred Stock
Time series to forecast: 8 Weeks

According to price forecasts, the dominant strategy among neural network is: SellHold


F(Polynomial Regression)6,7= p a 1 p a 2 p 1 n p j 1 p j 2 p j n p k 1 p k 2 p k n p n 1 p n 2 p n n X R(Modular Neural Network (Market News Sentiment Analysis)) X S(n):→ 8 Weeks e x rx

n:Time series to forecast

p:Price signals of NYCB^A stock

j:Nash equilibria (Neural Network)

k:Dominated move of NYCB^A stock holders

a:Best response for NYCB^A target price


A modular neural network (MNN) is a type of artificial neural network that can be used for news feed sentiment analysis. MNNs are made up of multiple smaller neural networks, called modules. Each module is responsible for learning a specific task, such as identifying sentiment in text or identifying patterns in data. The modules are then combined to form a single neural network that can perform multiple tasks. In the context of news feed sentiment analysis, MNNs can be used to identify the sentiment of news articles, social media posts, and other forms of online content. This information can then be used to filter out irrelevant or unwanted content, to identify trends in public opinion, and to target users with relevant advertising.5 Polynomial regression is a type of regression analysis that uses a polynomial function to model the relationship between a dependent variable and one or more independent variables. Polynomial functions are mathematical functions that have a polynomial term, which is a term that is raised to a power greater than 1. In polynomial regression, the dependent variable is modeled as a polynomial function of the independent variables. The degree of the polynomial function is determined by the researcher. The higher the degree of the polynomial function, the more complex the model will be.6,7

 

For further technical information as per how our model work we invite you to visit the article below: 

How do PredictiveAI algorithms actually work?

NYCB^A Stock Forecast (Buy or Sell) Strategic Interaction Table

Strategic Interaction Table Legend:

X axis: *Likelihood% (The higher the percentage value, the more likely the event will occur.)

Y axis: *Potential Impact% (The higher the percentage value, the more likely the price will deviate.)

Z axis (Grey to Black): *Technical Analysis%

Financial Data Adjustments for Modular Neural Network (Market News Sentiment Analysis) based NYCB^A Stock Prediction Model

  1. All investments in equity instruments and contracts on those instruments must be measured at fair value. However, in limited circumstances, cost may be an appropriate estimate of fair value. That may be the case if insufficient more recent information is available to measure fair value, or if there is a wide range of possible fair value measurements and cost represents the best estimate of fair value within that range.
  2. That the transferee is unlikely to sell the transferred asset does not, of itself, mean that the transferor has retained control of the transferred asset. However, if a put option or guarantee constrains the transferee from selling the transferred asset, then the transferor has retained control of the transferred asset. For example, if a put option or guarantee is sufficiently valuable it constrains the transferee from selling the transferred asset because the transferee would, in practice, not sell the transferred asset to a third party without attaching a similar option or other restrictive conditions. Instead, the transferee would hold the transferred asset so as to obtain payments under the guarantee or put option. Under these circumstances the transferor has retained control of the transferred asset.
  3. An entity may use practical expedients when measuring expected credit losses if they are consistent with the principles in paragraph 5.5.17. An example of a practical expedient is the calculation of the expected credit losses on trade receivables using a provision matrix. The entity would use its historical credit loss experience (adjusted as appropriate in accordance with paragraphs B5.5.51–B5.5.52) for trade receivables to estimate the 12-month expected credit losses or the lifetime expected credit losses on the financial assets as relevant. A provision matrix might, for example, specify fixed provision rates depending on the number of days that a trade receivable is past due (for example, 1 per cent if not past due, 2 per cent if less than 30 days past due, 3 per cent if more than 30 days but less than 90 days past due, 20 per cent if 90–180 days past due etc). Depending on the diversity of its customer base, the entity would use appropriate groupings if its historical credit loss experience shows significantly different loss patterns for different customer segments. Examples of criteria that might be used to group assets include geographical region, product type, customer rating, collateral or trade credit insurance and type of customer (such as wholesale or retail)
  4. When rebalancing a hedging relationship, an entity shall update its analysis of the sources of hedge ineffectiveness that are expected to affect the hedging relationship during its (remaining) term (see paragraph B6.4.2). The documentation of the hedging relationship shall be updated accordingly.

*International Financial Reporting Standards (IFRS) adjustment process involves reviewing the company's financial statements and identifying any differences between the company's current accounting practices and the requirements of the IFRS. If there are any such differences, neural network makes adjustments to financial statements to bring them into compliance with the IFRS.

NYCB^A New York Community Bancorp Inc. Depositary shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Series A Noncumulative Perpetual Preferred Stock Financial Analysis*

Rating Short-Term Long-Term Senior
Outlook*Ba3Ba3
Income StatementCaa2C
Balance SheetBa1Baa2
Leverage RatiosB3Baa2
Cash FlowBaa2B1
Rates of Return and ProfitabilityBa2Baa2

*Financial analysis is the process of evaluating a company's financial performance and position by neural network. It involves reviewing the company's financial statements, including the balance sheet, income statement, and cash flow statement, as well as other financial reports and documents.
How does neural network examine financial reports and understand financial state of the company?

References

  1. Jorgenson, D.W., Weitzman, M.L., ZXhang, Y.X., Haxo, Y.M. and Mat, Y.X., 2023. MRNA: The Next Big Thing in mRNA Vaccines. AC Investment Research Journal, 220(44).
  2. V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu, J. Veness, M. Bellemare, A. Graves, M. Riedmiller, A. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 02 2015.
  3. Matzkin RL. 2007. Nonparametric identification. In Handbook of Econometrics, Vol. 6B, ed. J Heckman, E Learner, pp. 5307–68. Amsterdam: Elsevier
  4. Dietterich TG. 2000. Ensemble methods in machine learning. In Multiple Classifier Systems: First International Workshop, Cagliari, Italy, June 21–23, pp. 1–15. Berlin: Springer
  5. B. Derfer, N. Goodyear, K. Hung, C. Matthews, G. Paoni, K. Rollins, R. Rose, M. Seaman, and J. Wiles. Online marketing platform, August 17 2007. US Patent App. 11/893,765
  6. Zubizarreta JR. 2015. Stable weights that balance covariates for estimation with incomplete outcome data. J. Am. Stat. Assoc. 110:910–22
  7. Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, et al. 2018a. Double/debiased machine learning for treatment and structural parameters. Econom. J. 21:C1–68
Frequently Asked QuestionsQ: Is NYCB^A stock expected to rise?
A: NYCB^A stock prediction model is evaluated with Modular Neural Network (Market News Sentiment Analysis) and Polynomial Regression and it is concluded that dominant strategy for NYCB^A stock is SellHold
Q: Is NYCB^A stock a buy or sell?
A: The dominant strategy among neural network is to SellHold NYCB^A Stock.
Q: Is New York Community Bancorp Inc. Depositary shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Series A Noncumulative Perpetual Preferred Stock stock a good investment?
A: The consensus rating for New York Community Bancorp Inc. Depositary shares each representing a 1/40th interest in a share of Fixed-to-Floating Rate Series A Noncumulative Perpetual Preferred Stock is SellHold and is assigned short-term Ba3 & long-term Ba3 estimated rating.
Q: What is the consensus rating of NYCB^A stock?
A: The consensus rating for NYCB^A is SellHold.
Q: What is the forecast for NYCB^A stock?
A: NYCB^A target price forecast: SellHold

Premium

  • Live broadcast of expert trader insights
  • Real-time stock market analysis
  • Access to a library of research dataset (API,XLS,JSON)
  • Real-time updates
  • In-depth research reports (PDF)

Login
This project is licensed under the license; additional terms may apply.